The present disclosure relates to coding video and analytical techniques to assess the quality of video obtained from coding and decoding operations.
Computers may employ a variety of objective metrics to assess the quality of video content and to help select video coding and processing parameters. These objective metrics typically focus on pixel differences across a frame, comparing source images to their counterparts after coding and decoding operations have been performed, or test video to reference video, and then sum or average the evaluated differences. Some examples of these objective metrics include, but are not limited to, Mean-Squared-Error (MSE) and Sum-of-Absolute Differences (SAD). Other more complex video quality metrics consider other statistical properties, human visual systems models, common coder/decoder distortion artifact models, and transformations between pixel and spatial frequency domains to isolate regions of interest for analysis.
Objective video quality metrics may not always correlate well with human subjective quality assessment of the same video for a number of reasons. A video may have significant artifacts from a full-reference pixel-difference perspective (MSE, SAD), but these artifacts may be difficult or impossible for a human viewer to observe if conditions are not favorable. Examples of unfavorable conditions include insufficient contrast ratio, extremes in light intensity (too dark or too bright), non-uniform content of the scene (e.g., water or clouds), and lacking familiar structural components (e.g., lines, edges, people, etc.), the artifact feature size is too small given the display resolution and/or viewing distance, the artifact did not persist in time long enough—collectively such conditions might lead to a relatively high subjective quality assessment despite the artifacts. Conversely, a video may have relatively few or relatively minor artifacts from a full-reference pixel-difference perspective, but these artifacts may be highly observable and objectionable if they exist for a sufficient period of time and are present on a focal object (e.g., a person's face)—such a video might lead to a relatively low subjective quality assessment despite relatively minor artifacts. Improvements in the correlation between objective video quality metrics and human subjective quality assessment have the potential to drive improvements in underlying video compression technologies, network bandwidth utilization, mobile device energy and resource utilization, and ultimately user experience with video related products and services.
Accordingly, what is needed is a system and method for improving objective video quality metric correlation to subjective metrics.